RRepoGEO

REPOGEO REPORT · LITE

CircleRadon/Osprey

Default branch main · commit be8f465d · scanned 6/3/2026, 3:57:56 AM

GitHub: 840 stars · 43 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface CircleRadon/Osprey, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Add a clear, concise project statement to the README's beginning

    Why:

    CURRENT
    The README currently starts with centered alignment tags and links, not a project statement.
    COPY-PASTE FIX
    Osprey: Pixel Understanding with Visual Instruction Tuning. This repository contains the official code for our CVPR 2024 paper, enabling AI models to interpret specific image regions using natural language prompts.
  • mediumhomepage#2
    Add the official paper link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/pdf/2312.10032.pdf
  • mediumtopics#3
    Expand repository topics to include broader vision-language terms

    Why:

    CURRENT
    mllm, pixel-understanding, sam, visual-instruction-tuning
    COPY-PASTE FIX
    mllm, pixel-understanding, sam, visual-instruction-tuning, vision-language-model, image-understanding

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface CircleRadon/Osprey
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
facebookresearch/segment-anything
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. facebookresearch/segment-anything · recommended 1×
  2. IDEA-Research/GroundingDINO · recommended 1×
  3. google-research/owlvit · recommended 1×
  4. openai/CLIP · recommended 1×
  5. run-llama/llama_index · recommended 1×
  • CATEGORY QUERY
    Need a tool for AI models to interpret specific image regions using natural language prompts.
    you: not recommended
    AI recommended (in order):
    1. Segment Anything Model (SAM) (facebookresearch/segment-anything)
    2. Grounding DINO (IDEA-Research/GroundingDINO)
    3. OWL-ViT (Open-World Vision Transformer) (google-research/owlvit)
    4. CLIP (Contrastive Language-Image Pre-training) (openai/CLIP)
    5. LlamaIndex (run-llama/llama_index)
    6. Hugging Face Transformers Library (huggingface/transformers)

    AI recommended 6 alternatives but never named CircleRadon/Osprey. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best visual instruction tuning approaches for advanced image understanding tasks?
    you: not recommended
    AI recommended (in order):
    1. LLaVA (haotian-liu/LLaVA)
    2. InstructBLIP (salesforce/LAVIS)
    3. MiniGPT-4 (Vision-CAIR/MiniGPT-4)
    4. OpenFlamingo (mlfoundations/open_flamingo)
    5. VisualGLM-6B (THUDM/VisualGLM-6B)
    6. PaliGemma

    AI recommended 6 alternatives but never named CircleRadon/Osprey. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of CircleRadon/Osprey?
    pass
    AI named CircleRadon/Osprey explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts CircleRadon/Osprey in production, what risks or prerequisites should they evaluate first?
    pass
    AI named CircleRadon/Osprey explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo CircleRadon/Osprey solve, and who is the primary audience?
    pass
    AI named CircleRadon/Osprey explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of CircleRadon/Osprey. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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CircleRadon/Osprey — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite